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Download DemoVendor: Microsoft
Certifications: Role-based
Exam Code: DP-100
Exam Name: Designing and Implementing a Data Science Solution on Azure
Updated: Mar 14, 2024
Q&As: 487
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HOTSPOT
You create an Azure Databricks workspace and a linked Azure Machine Learning workspace.
You have the following Python code segment in the Azure Machine Learning workspace:
import mlflowimport mlflow.azuremlimport azureml.mlflowimport azureml.core
from azureml.core import Workspace
subscription_id = 'subscription_id'resourse_group = 'resource_group_name'workspace_name = 'workspace_name'
ws = Workspace.get(name=workspace_name,subscription_id=subscription_id,resource_group=resource_group)
experimentName = "/Users/{user_name}/{experiment_folder}/{experiment_name}"mlflow.set_experiment(experimentName)
uri = ws.get_mlflow_tracking_uri()mlflow.set_tracking_uri(uri)
Instructions: For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
Correct Answer:
Box 1: No
The Workspace.get method loads an existing workspace without using configuration files.
ws = Workspace.get(name="myworkspace", subscription_id='
Box 2: Yes
MLflow Tracking with Azure Machine Learning lets you store the logged metrics and artifacts from your local runs into your Azure Machine Learning workspace.
The get_mlflow_tracking_uri() method assigns a unique tracking URI address to the workspace, ws, and set_tracking_uri() points the MLflow tracking URI to that address.
Box 3: Yes
Note: In Deep Learning, epoch means the total dataset is passed forward and backward in a neural network once.
Reference: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace.workspace https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while
others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You create a model to forecast weather conditions based on historical data.
You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.
Solution: Run the following code:
Does the solution meet the goal?
A. Yes
B. No
Correct Answer: B
Note: Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.
Compare with this example, the pipeline train step depends on the process_step_output output of the pipeline process step:
from azureml.pipeline.core import Pipeline, PipelineData from azureml.pipeline.steps import PythonScriptStep
datastore = ws.get_default_datastore()
process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output],
outputs=[process_step_output],
compute_target=aml_compute,
source_directory=process_directory)
train_step = PythonScriptStep(script_name="train.py",
arguments=["--data_for_train", process_step_output],
inputs=[process_step_output],
compute_target=aml_compute,
source_directory=train_directory)
pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata? view=azure-ml-py
You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary classification model. You use the Tune Model Hyperparameters module to tune accuracy for the model.
You need to select the hyperparameters that should be tuned using the Tune Model Hyperparameters module.
Which two hyperparameters should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Number of hidden nodes
B. Learning Rate
C. The type of the normalizer
D. Number of learning iterations
E. Hidden layer specification
Correct Answer: DE
D: For Number of learning iterations, specify the maximum number of times the algorithm should process the training cases.
E: For Hidden layer specification, select the type of network architecture to create. Between the input and output layers you can insert multiple hidden layers. Most predictive tasks can be accomplished easily with only one or a few hidden
layers.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two- class-neural- network
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